For different stages of business analytics huge amount of data is processed at various steps. Depending on the stage of the workflow and the requirement of data analysis, there are four main kinds of analytics – descriptive, diagnostic, predictive and prescriptive. These four types together answer everything a company needs to know- from what’s going on in the company to what solutions to be adopted for optimising the functions.
The four types of analytics are usually implemented in stages and no one type of analytics is said to be better than the other. They are interrelated and each of these offers a different insight. With data being important to so many diverse sectors- from manufacturing to energy grids, most of the companies rely on one or all of these types of analytics. With the right choice of analytical techniques, big data can deliver richer insights for the companies
Before diving deeper into each of these, let’s define the four types of analytics:
1) Descriptive Analytics: Describing or summarising the existing data using existing business intelligence tools to better understand what is going on or what has happened.
2) Diagnostic Analytics: Focus on past performance to determine what happened and why. The result of the analysis is often an analytic dashboard.
3) Predictive Analytics: Emphasizes on predicting the possible outcome using statistical models and machine learning techniques.
4) Prescriptive Analytics: It is a type of predictive analytics that is used to recommend one or more course of action on analyzing the data.
Let’s understand these in a bit more depth.
1. Descriptive Analytics
This can be termed as the simplest form of analytics. The mighty size of big data is beyond human comprehension and the first stage hence involves crunching the data into understandable chunks. The purpose of this analytics type is just to summarise the findings and understand what is going on.
2. Diagnostic Analytics
Diagnostic analytics is used to determine why something happened in the past. It is characterized by techniques such as drill-down, data discovery, data mining and correlations. Diagnostic analytics takes a deeper look at data to understand the root causes of the events. It is helpful in determining what factors and events contributed to the outcome. It mostly uses probabilities, likelihoods, and the distribution of outcomes for the analysis.
3. Predictive Analytics
As mentioned above, predictive analytics is used to predict future outcomes. However, it is important to note that it cannot predict if an event will occur in the future; it merely forecasts what are the probabilities of the occurrence of the event. A predictive model builds on the preliminary descriptive analytics stage to derive the possibility of the outcomes.
4. Prescriptive Analytics
The basis of this analytics is predictive analytics but it goes beyond the three mentioned above to suggest the future solutions. It can suggest all favorable outcomes according to a specified course of action and also suggest various course of actions to get to a particular outcome. Hence, it uses a strong feedback system that constantly learns and updates the relationship between the action and the outcome.
The four techniques in analytics may make it seem as if they need to be implemented sequentially. However, in most scenarios, companies can jump directly to prescriptive analytics. As for most of the companies, they are aware of or are already implementing descriptive analytics but if one has identified the key area that needs to be optimised and worked upon, they must employ prescriptive analytics to reach the desired outcome.